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2004 | 14 | 2 | 221-232
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Linear-wavelet networks

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This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.
Opis fizyczny
  • Instituto Tecnológico de Aeronáutica, Div. Engenharia Eletrônica, São José dos Campos - SP, 12228-900, Brazil
  • University of Reading, Department of Cybernetics, Reading RG6 6AY, United Kingdom
  • ISEL, Mechanical Engineering Studies Center, 1949-014 Lisboa, Portugal
  • ISEL, Mechanical Engineering Studies Center, 1949-014 Lisboa, Portugal
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